https://github.com/cicatriiz/google-embedding-utcp-manual
A UTCP manual for the Google Gemini embedding model, allowing any UTCP-compliant client to generate text embeddings.
https://github.com/cicatriiz/google-embedding-utcp-manual
ai embedding-model google-gemini machine-learning nlp utcp
Last synced: 4 months ago
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A UTCP manual for the Google Gemini embedding model, allowing any UTCP-compliant client to generate text embeddings.
- Host: GitHub
- URL: https://github.com/cicatriiz/google-embedding-utcp-manual
- Owner: Cicatriiz
- Created: 2025-07-24T23:17:54.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-07-24T23:21:26.000Z (10 months ago)
- Last Synced: 2025-08-10T07:40:06.097Z (10 months ago)
- Topics: ai, embedding-model, google-gemini, machine-learning, nlp, utcp
- Size: 1.95 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Google Embedding UTCP Manual
This project provides a UTCP (Universal Tool Calling Protocol) manual for the Google Gemini embedding model. This manual allows any UTCP-compliant client to generate text embeddings using the Gemini API without requiring any custom wrapper code.
## About the Google Embedding Model
Text embeddings are numerical representations of text that capture semantic meaning. These vector representations are crucial for a wide variety of NLP tasks, including:
- **Semantic Search**: Finding text that is conceptually similar, not just keyword-matched.
- **Classification**: Categorizing text into predefined labels.
- **Clustering**: Grouping similar texts together to identify trends.
- **Retrieval Augmented Generation (RAG)**: Enhancing language model outputs with factual information retrieved from a knowledge base.
The Google Gemini embedding model (`gemini-embedding-001`) is a state-of-the-art model that generates high-dimensional embeddings (3072 dimensions by default) to power these applications.
## About the Universal Tool Calling Protocol (UTCP)
UTCP is a modern, flexible, and scalable standard for defining and interacting with tools across various communication protocols. Its core philosophy is to **describe, not wrap**.
Instead of building and maintaining custom adapter servers for every tool, UTCP allows developers to create a simple "manual" (a `.json` file) that teaches a client how to call the tool's native endpoint directly. This approach has several key benefits:
- **No Wrapper Tax**: Eliminates the overhead of building and deploying adapter services.
- **Direct Communication**: Clients call tools directly, reducing latency.
- **Leverages Existing Infrastructure**: Uses the tool's native authentication, security, and rate-limiting.
- **Protocol Agnostic**: Works with any communication protocol (HTTP, WebSockets, CLI, gRPC, etc.).
## Usage
To use this manual, you will need a UTCP client. The client will load the `google-embedding-utcp-manual.utcp.json` file to discover the `embed_content` tool.
### Calling the `embed_content` Tool
Here is an example of how a client would call the `embed_content` tool defined in this manual:
```json
{
"tool": "embed_content",
"inputs": {
"model": "gemini-embedding-001",
"contents": ["Hello world"]
}
}
```
### Authentication
The `tool_provider` in the `google-embedding-utcp-manual.utcp.json` file uses a variable for the API key: `${GEMINI_API_KEY}`. Your UTCP client must be configured to replace this variable with your actual Gemini API key.